|本期目录/Table of Contents|

[1]王志波,王继柱.基于光纤光栅传感技术和卷积神经网络的铁路信号调节方法研究[J].工业仪表与自动化装置,2023,(01):91-96.[doi:10.19950/j.cnki.cn61-1121/th.2023.01.018]
　WANG Zhibo,WANG Jizhu.Research on railway signal regulation based on fiber grating sensing technology and convolutional neural network[J].Industrial Instrumentation & Automation,2023,(01):91-96.[doi:10.19950/j.cnki.cn61-1121/th.2023.01.018]
点击复制

基于光纤光栅传感技术和卷积神经网络的铁路信号调节方法研究

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2023年01期
页码:
91-96
栏目:
出版日期:
2023-02-15

文章信息/Info

Title:
Research on railway signal regulation based on fiber grating sensing technology and convolutional neural network
文章编号:
1000-0682（2023）01-0091-06
作者:
王志波; 王继柱
中国神华神朔铁路分公司，陕西 榆林 719316
Author(s):
WANG Zhibo ; WANG Jizhu
China Shenhua Shenshuo Railway Branch,Shanxi Yulin 719316,China
关键词:
光纤光栅传感器; 卷积神经网络; 铁路信号; 小波变换原理
Keywords:
fiber grating sensor;  convolutional neural network;  railway signal;  the principle of wavelet transform
分类号:
U216.3
DOI:
10.19950/j.cnki.cn61-1121/th.2023.01.018
文献标志码:
A
摘要:
为提高铁路信号瞬时频率识别的准确率，实现对铁路信号的故障分类和调节，该文设计了一种基于光纤光栅传感技术和卷积神经网络的铁路信号调节方法。首先通过光纤光栅传感器构建铁路信号光纤光栅传感监测模块，获取铁路信号数据，并对铁路信号数据进行预处理，运用小波变换原理提取处理后铁路信号数据的瞬时频率特征；然后组建基于卷积神经网络的铁路信号异常诊断模型，采用小波脊计算轨道电路移频信号的瞬时频率，诊断铁路信号故障，根据不同的故障类型，维护人员及时采用有效调节方法进行故障信号处理。最后以某市的某一线路列车作为测试对象，采集该列车的相关数据信息，并通过MATLAB软件进行仿真测试。实验表明，该方法可以准确地对铁路信号故障进行识别，维修人员能够及时采取不同调节措施对铁路故障信号进行调节，保障铁路运行的安全，具有一定的应用价值。
Abstract:
In order to improve the accuracy of instantaneous frequency identification of railway signals and realize fault classification and regulation of railway signals, a railway signal regulation method based on fiber grating sensing technology and convolutional neural network is designed in this paper. Firstly, the fiber Bragg grating sensor monitoring module of railway signal is constructed by fiber Bragg grating sensor to obtain the railway signal data, and preprocess the railway signal data. The instantaneous frequency characteristics of the processed railway signal data are extracted by using the wavelet transform principle; Then, a railway signal abnormality diagnosis model based on convolutional neural network is established. The instantaneous frequency of the frequency shift signal of the track circuit is calculated by wavelet ridge, and the railway signal fault is diagnosed. According to different fault types, the maintenance personnel timely adopt effective adjustment methods to deal with the fault signal. Finally, a certain line train in a city is taken as the test object, and the relevant data information of the train is collected, and the simulation test is carried out through MATLAB software. The experiment shows that this method can accurately identify the railway signal fault, and the maintenance personnel can take different adjustment measures to adjust the railway fault signal in time, so as to ensure the safety of railway operation. It has certain application value.

参考文献/References:

[1]林鹏,田宇,袁志明,等.高速铁路信号系统运维分层架构模型研究[J].自动化学报,2022,48(01):152-161.

[2]李刚,卢佩玲.基于数据驱动的高速铁路信号智能运维技术研究[J].铁道运输与经济,2021,43(10):61-67.
[3]郝艳捧,曹航宇,陈彦文,等.光纤光栅监测芯棒脆断预警和裂纹识别[J].中国电机工程学报,2022,42(07):2788-2797.
[4]秦俊非,毕江海,王继军,等.基于PLC的铁路信号机房焊接机器人控制系统设计[J].制造业自动化,2022,44(05):119-123.
[5]姚亚平.一种铁路信号安全控制平台的研究[J].铁道标准设计,2019,63(09):143-148.
[6]宁滨,莫志松,李开成.高速铁路信号系统智能技术应用及发展[J].铁道学报,2019,41(03):1-9.
[7]张振海,张湘婷.基于关联规则的铁路信号设备故障诊断方法[J].铁道标准设计,2022,66(04):175-181.
[8]李耀,张晓霞,郭进,等.铁路信号系统软件测试建模方法[J].西南交通大学学报,2022,57(02):392-400+424.
[9]屈松林,刘林.基于波形字典的铁路空口监测数据压缩算法[J].计算机应用研究,2020,37(S2):266-269+244.
[10]刘畅,赵显新,周兴林,等.光纤光栅的胎路三向力传感器的研究[J].计算机仿真,2019,36(11):101-104+125.
[11]李俊鑫,赖志华,强小俊,等.应用光纤光栅传感技术监测铁路钢桥螺栓断裂脱落的模型试验研究[J].铁道建筑,2020,60(01):40-43.
[12]张正义.基于光纤光栅的一体式靶式流量传感技术[J].发光学报,2020,41(02):217-223.
[13]王旭,孟德龙,李小康,等.基于自注入锁定的光纤光栅传感解调技术[J].激光与光电子学进展,2019,56(03):61-65.
[14]杨颖.分布式大容量光纤光栅传感网络的组网技术[J].光学技术,2019,45(06):712-717+736.
[15]宋力,余志武.基于光纤光栅传感技术的重载铁路预应力混凝土梁疲劳损伤试验研究[J].建筑结构学报,2019,40(01):58-66.

相似文献/References:

[1]张海静,姚博彬＊,武奇生.基于差分数据图和深度学习的短时交通流预测[J].工业仪表与自动化装置,2020,(02):3.
　ZHANG Haijing,YAO Bobin,WU Qisheng.Short-term traffic flow prediction based on the differential data graph and deep learning[J].Industrial Instrumentation & Automation,2020,(01):3.
[2]张晓华,马 煜,杨晨辉,等.基于卷积神经网络的设备安装位置智能识别方法[J].工业仪表与自动化装置,2021,(01):13.[doi:10.3969/j.issn.1000-0682.2021.01.003]
　ZHANG Xiaohua,MA yu,YANG Chenhui,et al.Intelligent identification method of equipment installation position based on convolution neural network[J].Industrial Instrumentation & Automation,2021,(01):13.[doi:10.3969/j.issn.1000-0682.2021.01.003]
[3]牟海维,段朝辉*,李 林,等.基于边缘特征和CNN联合的多视航拍图像配准方法[J].工业仪表与自动化装置,2021,(04):87.[doi:10.19950/j.cnki.cn61-1121/th.2021.04.018]
　MU Haiwei,DUAN Chaohui*,LI Lin,et al.Multi-view aerial image registration method based on edge feature and CNN[J].Industrial Instrumentation & Automation,2021,(01):87.[doi:10.19950/j.cnki.cn61-1121/th.2021.04.018]
[4]甘 李,姚 智,李 闯,等.基于卷积神经网络的汽轮机抗燃油泄漏智能预警技术研究[J].工业仪表与自动化装置,2022,(04):8.[doi:10.19950/j.cnki.cn61-1121/th.2022.04.002]
　GAN Li,YAO Zhi,LI Chuang,et al.Research on intelligent early warning technology of steam turbine anti fuel leakage based on convolutional neural network[J].Industrial Instrumentation & Automation,2022,(01):8.[doi:10.19950/j.cnki.cn61-1121/th.2022.04.002]
[5]李 娜,曹丽明.一种风力发电机轴承故障智能诊断方法[J].工业仪表与自动化装置,2022,(05):103.[doi:10.19950/j.cnki.cn61-1121/th.2022.05.019]
　LI Na,CAO Liming.An intelligent diagnosis method for wind turbine bearing fault[J].Industrial Instrumentation & Automation,2022,(01):103.[doi:10.19950/j.cnki.cn61-1121/th.2022.05.019]
[6]徐晓强,丁 峰,毕淑敏.基于高速通信的港口设备远程检测与控制技术研究[J].工业仪表与自动化装置,2024,(05):83.[doi:DOI:10.19950/j.cnki.cn61-1121/th.2024.05.016]
　XU Xiaoqiang,DING Feng,BI Shumin.Design of remote detection and control technology for port equipment based on high-speed mobile communication[J].Industrial Instrumentation & Automation,2024,(01):83.[doi:DOI:10.19950/j.cnki.cn61-1121/th.2024.05.016]

备注/Memo

备注/Memo:
收稿日期：2022-08-26
第一作者：
王志波（1983-），男，汉，陕西神木人，工程师，研究方向为铁路信号。
通信作者：
王继柱（1972-），男，汉，山西应县人，工程师，研究方向为铁路信号。
更新日期/Last Update: 1900-01-01